Image Classification - Jupyter Notebook

Trains is now ClearML

This documentation applies to the legacy Trains versions. For the latest documentation, see ClearML.

The example image_classification_CIFAR10.ipynb demonstrates integrating Trains into a Jupyter Notebook which uses PyTorch, TensorBoard, and TorchVision to train a neural network on the UrbanSound8K dataset for image classification. Trains automatically logs the example script's calls to TensorBoard methods in training and testing which report scalars and image debug samples, as well as the model and console log. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named image_classification_CIFAR10 which is associated with the Image Example project.

Another example optimizes the hyperparameters for this image classification example (see the Hyperparameter Optimization - Jupyter Notebook documentation page). This image classification example must run before the hyperparameter optimization example.


The accuracy, accuracy per class, and training loss scalars are automatically logged, along with the resource utilization plots (titled :monitor: machine), and appear RESULTS > SCALARS.


Debug samples

The image samples are automatically logged and appear in RESULTS > DEBUG SAMPLES.


By doubling clicking a thumbnail, you can view a spectrogram plot in the image viewer.



Trains automatically logs TensorFlow DEFINEs. A parameter dictionary is logged by connecting it to the Task using a call to the Task.connect method.

configuration_dict = {'number_of_epochs': 3, 'batch_size': 4, 'dropout': 0.25, 'base_lr': 0.001}
configuration_dict = task.connect(configuration_dict)  # enabling configuration override by trains

Parameter dictionaries appear in CONFIGURATIONS > HYPER PARAMETERS > General.


TensorFlow DEFINEs appear in the TF_DEFINE subsection.



Text printed to the console for training progress, as well as all other console output, appear in RESULTS > LOG.